System, method and computer program for improved forecasting residual values of a durable good over time

ABSTRACT

A residual value forecasting system may utilize heterogeneous data, such as used market data, industry-specific data, and non-industry-specific data, from disparate data sources to produce residual value forecasts of an item based on a sophisticated residual value forecasting model particularly configured for agility. The system can dynamically and quickly adapt to change in data inputs and produce custom outputs. The system may determine a baseline value for an item using the used market data, a microeconomic factor using the industry-specific data, and a macroeconomic factor using the non-industry-specific data, as well as adjustments such as locality adjustments and modifications. Given the macroeconomic factor and the microeconomic factor relative to the locality-adjusted value of the item and in view of the competitive sets of similar and/or substitute items in the same industry, the system can generate an accurate forecast residual value of the item at a future time point.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims a benefit of priorityunder 35 U.S.C. § 120 of the filing date of U.S. patent application Ser.No. 15/729,719, filed Oct. 11, 2017, entitled “SYSTEM, METHOD ANDCOMPUTER PROGRAM FOR IMPROVED FORECASTING RESIDUAL VALUES OF A DURABLEGOOD OVER TIME,” which claims a benefit of priority from U.S.Provisional Application No. 62/406,786, filed Oct. 11, 2016, entitled“SYSTEM, METHOD AND COMPUTER PROGRAM FOR IMPROVED FORECASTING RESIDUALVALUES OF A DURABLE GOOD OVER TIME,” and which is a continuation-in-partof U.S. patent application Ser. No. 15/423,026, filed Feb. 2, 2017,entitled “SYSTEM, METHOD AND COMPUTER PROGRAM FOR FORECASTING RESIDUALVALUES OF A DURABLE GOOD OVER TIME,” which is a continuation of U.S.patent application Ser. No. 13/967,148, filed Aug. 14, 2013, now U.S.Pat. No. 9,607,310, entitled “SYSTEM, METHOD AND COMPUTER PROGRAM FORFORECASTING RESIDUAL VALUES OF A DURABLE GOOD OVER TIME,” which claims abenefit of priority from U.S. Provisional Application No. 61/683,552,filed Aug. 15, 2012, entitled “SYSTEM, METHOD AND COMPUTER PROGRAM FORFORECASTING RESIDUAL VALUES OF A DURABLE GOOD OVER TIME.” Allapplications listed in this paragraph are hereby fully incorporated byreference herein for all purposes.

TECHNICAL FIELD

This disclosure relates generally to forecasting future market value ofdurable goods, and more particularly to improved systems, methods andcomputer program products for forecasting the value of an item usingmicroeconomic, macroeconomic, and competitive set information andupdating the forecast value at predetermined time intervals.

BACKGROUND OF THE RELATED ART

The market value of an item is known at the time that it is sold to aconsumer. After this initial transaction, however, the value of the itemwill decline. The amount by which the value decreases may depend uponmany factors, such as the amount of time that has passed since theoriginal sale, the amount of wear experienced by the item, and so on.

Because of the difficulty of determining these factors with anycertainty, the value of an item after its initial sale is conventionallydetermined by resale values of the item. For instance, the value of atwo-year-old automobile is determined by examining the prices for whichsimilarly equipped automobiles of the same make, model and year haveactually sold. While some adjustments may be made to these values (e.g.,for vehicle mileage above or below some average range), determination ofthe automobile's value generally relies on past resale prices of thesame vehicle.

Since these conventional methods of determining the value of an item arerelatively simplistic and take into account only backward-looking data(e.g., past sales of the item), they are not as accurate as may bedesired. For instance, an automobile leasing company may need to knowthe future value of the automobiles that it owns in order to obtainfinancing for expansion or other business transactions. It wouldtherefore be desirable to provide improved methods for determining thefuture value of such items.

SUMMARY OF THE DISCLOSURE

This disclosure is directed to new and improved systems, methods andcomputer program products for forecasting future values of an item thatsolve one or more of the problems discussed above. An object of theinvention is to provide realistic and adjusted residual values of adurable good (item) over the item's lifecycle to reflect the market,incentives and purchases. Another object of the invention is to provideaccurate, reliable residual values across items being valued such thatmanufacturers can market their items with clear, consistent messagesbased on accurate, reliable forecasts. Yet another object of theinvention is to provide relevant and timely residual values that reflectproduct enhancements, packaging, and/or content adjustments made toitems being valued. Yet another object of the invention is to provideresidual values that have utility to each manufacturer's ecosystem. Suchresidual values may encompass all phases of a durable good sales cycle,for instance, from dealer engagement, manufacturer support, cooperationon pricing, to off-lease supply management.

These and other objects of the invention may be realized in a residualvalue forecasting system embodied on one or more server machinesparticularly configured for generating forecasted future values(residual values) of an item, for instance, a high-value durable goodsuch as a vehicle. The system may utilize various types of data receivedand/or obtained from disparate data sources over a network to producevariations of residual value forecasts of the item based on a new andimproved residual value forecasting model. Particularly configured foragility, the system can dynamically and quickly adapt to change in datainputs and produce new outputs (referred to herein as “deliverables”),such as a blended or customized forecast, to client devices. In additionto agility, the new and improved residual value forecasting modeldisclosed herein can also change how deliverables are produced byimplementing a significantly more sophisticated residual valueforecasting algorithm.

In some embodiments, a residual value forecasting method implementing aspecial residual value forecasting algorithm may include receiving, by asystem implementing the method and operating in a network computingenvironment, a request from a client device for a residual valueforecast of an item. For the purpose of illustration, and not oflimitation, the item can be a vehicle or any high-value durable goodthat does not wear out quickly or that yields utility over time.Responsively, the system may determine a baseline value for the vehicle,based on a given configuration of the vehicle, and determine a referenceperiod at which adjustments to the baseline value may be made. Thereference period may begin at an initial time and ends a period of timefrom the initial time (“referred to as the forecast time”). The initialtime may be the day of the request or a day in the past. The period oftime may be a number of months such as 24-month, 36-month, etc.

The residual value forecasting method may further comprise determininglocality adjustment(s) to the vehicle; collecting or estimatingincremental values of modifications to the base configuration of thevehicle; determining a locality-adjusted value of the modified vehicle;constructing competitive sets of similar and/or substitute vehicles inthe same industry, for instance, the used vehicle industry; collectingmacroeconomic data and determining a macroeconomic factor based on thecollected macroeconomic data; collecting microeconomic data anddetermining a microeconomic factor based on the collected microeconomicdata; and generating a forecast residual value of the vehicle at theforecast time, given the macroeconomic factor and the microeconomicfactor relative to the locality-adjusted value of the modified vehicleand in view of the competitive sets of similar and/or substitutevehicles in the same industry.

In some embodiments, the residual value forecasting method may furthercomprise performing at least a quality assurance process. The qualityassurance process may entail comparing the forecast residual value ofthe vehicle with residual values of vehicles in the competitive sets,computing adjustments accordingly, and generating a final residual valuefor the vehicle.

In some embodiments, the residual value forecasting method may leveragelinear regression modeling techniques to provide purely data sciencedriven outputs with high R-squared values, for instance, at leastapproximately 80% to 85%. Skilled artisans appreciate that linearregression calculates an equation that minimizes the distance between afitted line and all of the data points. R-squared is a statisticalmeasure of how close the data are to the fitted regression line. In thecontext of this disclosure, this statistical measure providesquantitative evidence in how the new and improved residual valueforecasting model can alone explain a significantly higher percentage ofthe variance in the dependent variable, without user intervention,oversight processing, or any qualitative feedback cycle (referred toherein as “qualitative input”) to the model output. As skilled artisanscan appreciate, high reliance on qualitative input can affect accuracyof values in a negative way based on processing inefficiencies.

The significant reduction of non-efficient qualitative input enables asystem implementing the residual value forecasting method disclosedherein to perform significantly more efficiently and reduce processingtimes and resources such as computer systems used. The system mayoptionally allow efficient qualitative input, if desired. Efficientqualitative input may be much more dedicated to non-processing matterssuch as efficient quality assurance (QA) of the output. This focus, inturn, can result in producing more accurate and higher quality output.

One embodiment may comprise a system having a processor and a memory andconfigured to implement a method disclosed herein. One embodiment maycomprise a computer program product that comprises a non-transitorycomputer-readable storage medium which stores computer instructions thatare executable by at least one processor to perform the method. Numerousother embodiments are also possible.

These, and other, aspects of the disclosure will be better appreciatedand understood when considered in conjunction with the followingdescription and the accompanying drawings. It should be understood,however, that the following description, while indicating variousembodiments of the disclosure and numerous specific details thereof, isgiven by way of illustration and not of limitation. Many substitutions,modifications, additions and/or rearrangements may be made within thescope of the disclosure without departing from the spirit thereof, andthe disclosure includes all such substitutions, modifications, additionsand/or rearrangements.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the invention may become apparent uponreading the following detailed description and upon reference to theaccompanying drawings.

FIG. 1 depicts a diagrammatic representation of an example of systemarchitecture, according to some embodiments disclosed herein.

FIG. 2 depicts a diagrammatic representation of an example of varioustypes of data collected by an example of an enterprise computer systemin which embodiments disclosed herein may be implemented.

FIG. 3A depicts a diagrammatic representation of an example of a networkcomputing environment implementing a variety of processes particularlyconfigured for processing various types of data from disparate datasources and providing outputs to client device(s), according to someembodiments disclosed herein.

FIG. 3B depicts a diagrammatic representation of an example of a networkcomputing environment similar to the network computing environment shownin FIG. 3A, without certain specific types of data, according to someembodiments disclosed herein.

FIG. 3C depicts a plot diagram comparing two residual curves generatedwith (FIG. 3A) and without (FIG. 3B) certain specific types of data,according to some embodiments disclosed herein.

FIG. 4 is a process flow illustrating the acquisition of various typesof data from disparate data sources and preparation of input data for aresidual value forecasting method, according to some embodimentsdisclosed herein.

FIG. 5 is a process flow illustrating an example of a residual valueforecasting method, according to some embodiments disclosed herein.

FIG. 6 is a flow diagram illustrating an example of a method foroptionally revising a generated residual value curve based onqualitative input via a feedback cycle, according to some embodimentsdisclosed herein.

FIG. 7 is a flow diagram illustrating an example of a method foroptionally allowing a client to provide qualitative input on a generatedresidual value curve, according to some embodiments disclosed herein.

FIG. 8 depicts a plot diagram illustrating an example of a residualvalue curve, according to some embodiments disclosed herein.

FIG. 9 depicts a plot diagram illustrating an example of a residualvalue curve adjusted based on competitive set comparison, according tosome embodiments disclosed herein.

FIG. 10 depicts a bar diagram illustrating the effects of modificationadjustments and locality adjustments, according to some embodimentsdisclosed herein.

FIG. 11 depicts a plot diagram illustrating percentage pointsadjustments by factor, according to some embodiments disclosed herein.

FIG. 12 depicts a diagrammatic representation of a user interface of aworkbench application, according to some embodiments disclosed herein.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Embodiments of the invention and various features and advantageousdetails thereof are explained more fully with reference to thenon-limiting embodiments that are representatively illustrated in theaccompanying drawings and detailed in the following description.Descriptions of well-known starting materials, processing techniques,components and equipment are omitted so as not to unnecessarily obscurethe invention in detail. It should be understood, however, that thedetailed description and specific examples, while indicating exemplaryand representative embodiments of the invention, are given by way ofillustration only and not by way of limitation. Various substitutions,modifications, additions or rearrangements are within the spirit orscope of this disclosure and will become apparent to those skilled inthe art from this disclosure.

For the purposes of this disclosure, the term “item” may be used torefer to a durable good, product, or any item that has a known value atthe time it was first sold and that has a different resale value overtime thereafter. Examples of an item may include a vehicle, a realestate property, etc.

The resale value of an item may be affected by various factors such astime, the availability of same or similar items, the geographicallocation where the item physically resides, demand in for the item inthe resale market and/or industry, the purchasing power of the targetbuyers, and so on. An ability to determine the amount by which the itemwill change (e.g., devalue) over time, and thereby forecast the resaleor residual value of the item can provide a better understanding of acompany's assets and can allow the company to make better decisions.

FIG. 1 depicts a diagrammatic representation of an example of systemarchitecture, according to some embodiments disclosed herein. Forpurposes of clarity, a single client computer 110, a single servercomputer 140, and a single data source 160 are shown in the example ofFIG. 1. Client and server computers 110, 140, and data source 160 eachrepresents an exemplary hardware configuration of a data processingsystem capable of bi-directionally communicating with other networkedsystems and devices over a network such as the Internet. Those skilledin the art will appreciate that enterprise computing environment 130 maycomprise multiple server computers, and multiple client computers anddata sources may be bi-directionally coupled to enterprise computingenvironment 130 over network 120.

Client computer 110 can include central processing unit (“CPU”) 111,read-only memory (“ROM”) 113, random access memory (“RAM”) 115, harddrive (“HD”) or storage memory 117, and input/output device(s) (“I/O”)119. I/O 119 can include a keyboard, monitor, printer, and/or electronicpointing device. Example of I/O 119 may include mouse, trackball,stylist, or the like. Client computer 110 can include a desktopcomputer, a laptop computer, a personal digital assistant, a cellularphone, or nearly any device capable of communicating over a network.Server computer 140 may have similar hardware components including CPU141, ROM 143, RAM 145, HD 147, and I/O 149. Data source 160 may includea server computer having hardware components similar to those of clientcomputer 110 and server computer 140, or it may be a network-enableddata storage device.

Each computer shown in FIG. 1 is an example of a data processing system.ROM 113 and 143, RAM 115 and 145, HD 117 and 147, and database 150 caninclude media that can be read by CPU 111 and/or 141. Therefore, thesetypes of computer memories exemplify non-transitory computer-readablestorage media. These memories may be internal and/or external tocomputers 110 and/or 140.

Portions of the methods described herein may be implemented in suitablesoftware code that may reside within ROM 143, RAM 145, HD 147, database150, or a combination thereof. In some embodiments, computerinstructions implementing an embodiment disclosed herein may be storedon a direct access storage device (DASD) array, magnetic tape, floppydiskette, optical storage device, or any appropriate non-transitorycomputer-readable storage medium or storage device. A computer programproduct implementing an embodiment disclosed herein may thereforecomprise one or more computer-readable storage media storing computerinstructions translatable by CPU 141 to perform an embodiment of amethod disclosed herein.

In an illustrative embodiment, the computer instructions may be lines ofcompiled C++, Java, or other language code. Other architectures may beused. For example, the functions of server computer 140 may bedistributed and performed by multiple computers in enterprise computingenvironment 130. Accordingly, each of the computer-readable storagemedia storing computer instructions implementing an embodiment disclosedherein may reside on or accessible by one or more computers inenterprise computing environment 130.

FIG. 2 depicts a diagrammatic representation of an example of varioustypes of data collected by an example of enterprise computer system 140in which embodiments disclosed herein may be implemented. In thisexample, enterprise computer system 140, which may be embodied on one ormore server machines operating in enterprise computing environment 130,may receive, obtain, or otherwise collect various types of data 161-166.Before describing these data types in detail, an overview of residualvalue forecasting methodology may be helpful.

The current market value of a durable good (“item”) is known at the timeof sale, t₀, but its resale value at some future time points, t_(n)>t₀,may be largely unknown. In this disclosure, a forecast of such a resalevalue can be generated by computing a special function with estimatedcoefficients.

An ability to forecast the resale—or “residual”—value of item provides abetter understanding of the amount by which the item will devalue overfixed interval of time. If the aim is to determine the amount ofdevaluation of an item that will occur between time period m and n(Δ_((n,m))=t_(n)−t_(m)), one must compute:

ΔV _((n,m)) =V _(i,n) −V _(i,m)   [Equation 1]

where V_(i,m)=the value of item i at time t_(m)

-   -   V_(i,n)=the value of item i at time t₀.

Though the change in valuation between any single time point and afuture time point requires that Δ_((n,m))=(t_(n)−t_(m)) be greater thanor equal to 0, there is no restriction on the algebraic sign of thechange in value during that time period as an item may increase in valueas time elapses. Briefly referring to FIG. 8, the market value of item iin the current period, t₀, is V_(i,0) but continually declines overtime. After a period Δ_((n,m))=(t₁−t₀), the change in value of item i isΔV_(i,(n,m))=V_(i,1)−V_(1,0)<0. The change in value betweenΔ_((n,m))=(t₂−t₀) is ΔV_(i,(n,m))=V_(i,2)−V_(i,0)<0 and the change invalue between Δ_((n,m))=(t₂−t₁) is ΔV_(i,(n,m))=V_(i,2)−V_(i,1)<0.Though the devaluation over time requires that Δ_((n,m))=(t_(n)−t_(m))be greater than or equal to 0, there is no restriction on the algebraicsign of the change in value during that time period as an item mayincrease in value as time elapses.

A major complication that arises in determining the residual value of anitem at a future time point, V_(i,n), will not actually be known untilt_(n). This complication suggests that some type of forecasting must beconducted in order to estimate residual values in time periods that havenot yet been reached. This disclosure provides a methodology forforecasting residual values in two time periods, t_(m) and t_(n), andenables the construction of a change in valuation metric ΔV_(i(n,m)). Byestimating the changes in value for successive future time intervals,one can then construct a function that captures the estimatedrelationship between time and the item's value. In this approach, aresidual value forecasting model is built to predict V_(i,n) for anytime period 0≤n≤T. As forecast interval is relative to the baseline,Δ_(9n,0))=(t_(n)−t₀), the farther away in time a forecast is relative tothe baseline, the more uncertainty will exist. Accordingly, theforecasting error ε_(n,0) will grow as the width of the time interval,66 _((n,0)), increases.

Taking this uncertainty into consideration, embodiments utilizedifferent types of data to aid in forecasting residual values of an itemover time. Example data types include, but are not limited to,modifications to the items, locality of the items, microeconomicfactors, macroeconomic factors, and sets of competitive items. Specialvariables representing these data types will be discussed in more detailbelow.

Modifications 161 reflect any changes to item i that may affect itsvalue at any time point. Examples of modifications (M_(i)) includeoptions added to the item in prior periods, differentconfigurations/styles of the item, or other features which maydistinguish one item from another that is produced by the samemanufacturer.

Locality 162 represents valuation differences of item i in an industry(p), the valuation differences varying geographically (i ∈ p). Examplesof Locality (L_(p)) would include adjustments to equalize sales of theessentially identical items made in different locations, allowingvaluation to be conducted, for instance, at both the national andstate/province levels.

Depreciation 163 represents the natural change in value that occurs asitem i is used over time. Depreciation (D_(i)) can be determined frompast sales of the item. Some embodiments of a residual value forecastingmethod may not need to rely on D_(i) in generating a residual valueforecast.

Microeconomic 164 represents information specific to the industry p towhich item i is associated (i ∈ p). For example, microeconomic factors(G_(p)) may include supply and/or demand specific to the industry p,industry trends, seasonality, and/or volatility of the item, orinformation about a company that is in the industry p. For example,segment supply and model supply are specific to the automotive industryand thus are considered microeconomic factors specific to the automotiveindustry. In contrast, the overall industry supply is considered amacroeconomic impact factor as it affects the overall automotiveeconomy.

Macroeconomic 165 represents information that is non-specific to theitem and/or its industry. Macroeconomic factors (F) may relate to theoverall economy, rather than to the specific industry with which item iis associated (e.g., the real estate or automotive industries). Examplesof macroeconomic information may include gas prices, inflation,unemployment rate, interest rates, industry-wide used market supply,etc. All vehicles (e.g., fleet vehicles, lease/loan financed vehicles,cash paid vehicles) are generally expected to return to the used market(e.g., used vehicles offered at dealers, used vehicles transacted fromprivate parties to private parties, etc.) in a given period of time withcertain ages (e.g., 1-5 year-old vehicles). As an example, when a newcar is leased, at some point in time that vehicle is expected to bereturned to a bank after the lease is up and the returned vehicle mostlikely will be offered in the used market for sale by a dealer. Suchitems or units in the overall industry-wide used market supply can be inthe millions. For example, over 11 million units of vehicles ages 1-5years can be expected to return every year to the used market.

Competitive sets 166 represent information that relates to items thatcompete with the item of interest. Competitive sets (C_(iU)) include allother items, j=1, . . . ,J (i≠j), in the same industry p and in thecompetitive set U (i, j ∈ U ∀ j) which are similar and/or are reasonablesubstitutes for item i being valued. Examples of competitive items j mayinclude items produced by different manufacturers that sharesimilarities (e.g., similar vehicle attributes such as miles per gallon,engine type, transmission type, sports package, weather package,technology package, etc.) with item i being valued. Competitive sets 166may also include information relating to sales incentives applied tocompetitive items j. Competitive sets 166 may further includeinformation relating to sales or recall information for competing items.

Leveraging these particular data types, the new and improved residualvalue forecasting model described below can be applied to any durablegood that is, items not immediately consumed and retaining somenon-negative value over time. In some embodiments, model variablerepresenting the particular data types described above may encompass thevarious components of the residual value forecasting model required tovalue item i in any industry p. Specifically, the microeconomic (G_(p)),Locality (L_(p)), and competitive sets (C_(iIU)) components are specificto an industry p pertaining to item i that is being valued as long asall other members, j=1, . . . , J, of the competitive set U are in thesame industry, p, as item i.

A system implementing the new and improved residual value forecastingmodel may operate to quickly adapt to different types of input data and,as such, can dynamically produce differentiating outputs (also referredto as “deliverables” or “information products”) useful for variouspurposes such as data analyses. This useful agility and flexibility ofthe new system is illustrated in FIGS. 3A-3C.

FIG. 3A depicts a diagrammatic representation of an example of a networkcomputing environment 130 having residual value forecasting system 140embodied on one or more server machines and implementing a variety ofprocesses 400, 500, 600, and 700 particularly configured for processingvarious types of data received, obtained, or otherwise collected(simultaneously, periodically, continuously, or at differenttimes/frequencies such as daily, weekly, monthly, quarterly, etc. overvarious communications channels and protocols such as File TransferProtocol (FTP)) over network 120 from disparate data sources 361, 363,365, 367, and 369 (which can, for instance, include a FTP server) andproviding outputs 300 b to client device(s) 110, according to someembodiments disclosed herein. Processes 400, 500, 600, and 700 aredescribed in detail below. In some embodiments, raw data from disparatedata sources 361, 363, 365, 367, and 369 can be stored in database 150.In some embodiments, processed data from processes 400, 500, 600, and700 can be stored in database 150.

As illustrated in FIG. 3A, examples of types of data that may bereceived, obtained, or otherwise collected from various data sources mayinclude data specific to an industry relating to a particular item(referred to herein as “industry-specific data”) and data not specificto any industry (referred to herein as “non-industry-specific data”)such as inflation, unemployment rate, etc. Additionally, residual valueforecasting system 140 may receive, obtain, or otherwise collectdifferent types of auction data and certified data. For example,residual value forecasting system 140 may receive, obtain, or otherwisecollect open auction data, closed auction data, and certified pre-owndata. Skilled artisans appreciate that, although FIG. 3A shows a datasource per data type, this need not be the case. A single data sourcemay provide residual value forecasting system 140 with one or more ofthese data types and multiple data sources may provide residual valueforecasting system 140 with the same type of data. Accordingly, FIG. 3Ais meant to be illustrative and non-limiting.

Similarly, FIG. 3B depicts a diagrammatic representation of residualvalue forecasting system 140 that receive, obtain, or otherwise collectvarious types of data from data sources 361, 363, and 369. In thisexample, residual value forecasting system 140 may consider the varioustypes of data from data sources 361-369 in determining a residual valueforecast for an item i, but may not include closed auction data and/orcertified pre-owned data in its computation.

In the past, residual values of an item were calculated withsignificantly less heterogeneous data types than those shown in FIGS. 3Aand 3B. For example, a system programmed to compute residual values fora used vehicle may rely solely on the wholesale/auction prices of usedvehicles. This limits the system to rigidly producing a single type ofoutput residual values for a used vehicle. Embodiments of a residualvalue forecasting system (e.g., residual value forecasting system 140)disclosed herein is particularly programmed to operably take (and/orreceive) a variety of data from disparate upstream data sources (e.g.,data sources 361-369 shown in FIG. 3A or FIG. 3B, explained above),process them accordingly (explained below), and utilize them in variouscomputations to produce information products (e.g., a custom outputtailored to a customer's request, see e.g., FIG. 3C) which can havedifferent utilities in downstream applications/scenarios. Thesecomputational processes allow the system to be agile, flexible, androbust in creating useful information products, not just for thewholesale/auction used vehicle market, but also for other industriessuch as the retail vehicle market, vehicle data providers, vehicle leasemanagement, fleet management, etc. The impact of the system can besignificant. For instance, a single point of residual value can have abillion dollar impact in the automotive marketplace.

FIG. 3C depicts a plot diagram comparing two residual curves generatedwith (e.g., FIG. 3A) and without (e.g., FIG. 3B) certain specific typesof data, according to some embodiments disclosed herein. As illustratedin FIG. 3C, when residual value forecasting system 140 includes closedauction data and certified pre-owned data in addition to open auctiondata and other factors in determining a residual value forecast (curve)for item i over a 60-month period, custom output 300 a consistentlyprovides forecasted residual values higher than those indicated byoutput 300 b. In some cases, one or both outputs (information products)may be presented (e.g., via a user interface) on a client device,allowing a user to utilize the output(s) to view, analyze, and/or takeappropriate action such as setting a required bank reserve, asexemplified below.

Skilled artisans appreciate that there can be many useful applicationsof embodiments disclosed herein. For example, residual values generatedby exemplary residual value forecasting system 140 disclosed herein(e.g., outputs 300 a and 300 b illustrated in FIG. 3C), can be used toestimate the value of automobiles over time and therefore allow one todetermine the resale value that could be expected at future time points.Examples of automobiles may include nearly all passenger and lighttrucks available to consumers in the United States and Canada.Furthermore, the generated residual values can provide guidelines forpricing fixed-term vehicle leases which captures the expected change invalue that will result in the time interval between the leased vehicle'sacquisition at time to and its disposition at time t_(d). In someembodiments, not only the estimated residual value of item i can beprovided at disposition (V_(i,d)), but forecasted values of item i canalso be provided at equally-spaced fixed time points between t₀ andt_(d), thereby allowing construction of a residual curve that capturesthe relationship between vehicle value and time. Over time, and as newinformation becomes available, residual value forecasting system 140 mayupdate the stored forecasts to reflect changing values of exogenousmacroeconomic and industry-specific microeconomic variables andvehicle-specific, endogenous variables (e.g., depreciation, competitivesets, modifications, etc.).

Referring to FIGS. 4-7, examples of processes 400, 500, 600, and 700 areshown. Processes 400, 500, 600, and 700 may be implemented, for example,in residual value forecasting system 140 as shown in FIG. 1. It shouldbe noted that the particular steps illustrated in FIGS. 4-7 areexemplary, and the steps of alternative embodiments may vary from thoseshown in FIGS. 4-7.

Referring to FIG. 4, process flow 400 illustrates the acquisition ofvarious types of data from disparate data sources and preparation ofinput data for a residual value forecasting method (e.g., process 500shown in FIG. 5, described below), according to some embodimentsdisclosed herein. In some embodiments, process 400 may be part of aresidual value forecasting system embodied on one or more servercomputer (e.g., enterprise computer system 140) operating in anenterprise computing environment (e.g., enterprise computing environment130) and specially programmed to implement a residual value forecastingmethod disclosed.

The residual value forecasting system may initially query data source(s)for information of various types described above (405). The data sourcesmay include those (e.g., data storage units) that are internal to theenterprise computing environment and those that are external to theenterprise computing environment. In one embodiment, the residual valueforecasting system may employ data crawlers that are particularlyprogrammed to programmatically and automatically (e.g., periodically orcontinuously) query external data sources, including those operating indisparate network computing environments and conditions, searching forinformation relevant to generating a certain forecast, for instance,responsive to a request for a custom forecast from a client devicecommunicatively connected to the residual value forecasting system overa network. The request from the client device may include information ona particular vehicle Year/Make/Model/Type and a specified time period.Optionally, the request may indicate a desired data type or data typesto be used in generating the forecast. Alternatively or additionally,the residual value forecasting system may systematically andautomatically generate various forecasts estimating the values ofdifferent vehicle Years/Makes/Models/Types over different time periodsand lengths and may push the various forecasts thus generated todifferent client devices (which can be owned and operated by differententities/owners). Optionally, a registered user (e.g., a subscriber) whohas an account with the residual value forecasting system may log inremotely to search and/or review a particular forecast or forecastsgenerated by the residual value forecasting system.

The residual value forecasting system may receive, obtain, or otherwisecollect the data from these data sources (410) and store the collecteddata for further processing (415). The collected data is examined by theresidual value forecasting system (420) and processed to identifyportions of the data that will be used to generate the forecast.

The data may be “scrubbed” by the residual value forecasting system(425) in order to provide a better basis for the forecast. The scrubbingprocess may involve the residual value forecasting system performingvarious techniques to improve the quality of the data, such asidentifying data that appears to be in error, removing outlying datapoints that substantially deviate from the remainder of the data, and soon. The data may also be filtered or examined by the residual valueforecasting system to identify particular fields or types of data withinthe data that has been collected by the residual value forecastingsystem.

Still further, the residual value forecasting system may transform allor part of the collected data into forms (e.g., data representationshaving a normalized and/or common data structure internal to theresidual value forecasting system) that are suitable for use/consumptionby the residual value forecasting system. Such forms can include datastructures for mapping incoming vehicle data to a vehicle code system(e.g., ALG vehicle code system), cleaning up data issues (e.g., manualentry errors that exist in the incoming vehicle data), adjustingtransaction prices to certain assumptions such as normalized mileage peryear, etc. After the desired data has been selected and scrubbed, ifnecessary, the modified data set can be stored (430) in a local datastorage device, from which it can be retrieved and used by the residualvalue forecasting system in the generation of the forecast.

FIG. 5 is a process flow illustrating an example of residual valueforecasting method 500, according to some embodiments disclosed herein.In this example, residual value forecasting method 500 may comprisedetermining a baseline value for an item with a base configuration(501); determining a reference period at which adjustments are to bemade to the item (503); determining a constant width of time intervalsat which forecasts are to be generated for the item (505); determininglocality adjustment(s) to the item (507); collecting or estimatingincremental values of modifications to the base configuration of theitem (509); determining a locality-adjusted value of the modified itemat the forecast time (511, see, e.g., FIG. 10); constructing competitivesets of similar and/or substitute items in the same industry (513);collecting macroeconomic data and determining a macroeconomic factor(515); collecting industry-specific microeconomic data and determining amicroeconomic factor (517); and generating a residual value of item atthe forecast time (519, see, e.g., FIG. 11). Optionally, residual valueforecasting method 500 may further comprising performing one or morequality assurance (QA) operations on the generated output (521, see,e.g., FIG. 12) and adjusting, if necessary, to generate a final forecastof residual value of the item (523, see, e.g., FIG. 3C). Note thatconstruction of the residual value forecasts requires performing somesteps at certain milestones in the lifetime of the item (e.g., at to andat any time period when any modification is made), while others may beperformed at each time period for which the item's value is to beforecasted. The steps are further described in detail below.

In some embodiments, a system implementing residual value forecastingmethod 500 may determine a baseline value for item i with a baseconfiguration (501), for instance, by taking an h-month historicalaverage (V_(i,h)) of data points of particular data types (e.g., usedmarket values, wholesale/auction values, etc.) collected by the system.This operation may be triggered by a request from a client devicecommunicatively connected to the system over a network, by aninstruction or command from an administrator of the system (e.g., via anadministrative tool of the system), or automatically by a programmedtrigger or scheduled event.

Under most circumstance, recent historical market values are availablefor computing V_(i,h) (which represents the h-month historical averagebaseline value for item i with a base configuration). V_(i,h) may beexpressed below as a function of time, t_(n) (n=0, . . . , T), taking ah-month historic average of the market values of item i at time t₀before modifications.

V _(i,h)−(V _(i,0) +M _(i,n))×(τ_(i,n) ×L _(p,n))+(β₁ ΔF _(n|n−h)+β₂ ΔG_(p,n|n−h))+C _(iU,n|n*)   [Equation 2.1]

V _(i,h) =BV _(i,0)+(β₁ ΔF _(n n−h) +β ₂ ΔG _(p,n|n−h))+C_(iU,n|n*)  [Equation 2.2]

where h=1, . . . , H. As a non-limiting example, H may represent a valueof 24.

Equation 2.2 represents another way to express V_(i,h) where(V_(i,0)+M_(i,n))×(τ_(i,n)×L_(p,n))=BV_(i,0). These special modelvariables are described in more detail below.

V_(i,0) represents an initial value at the beginning of the estimationperiod, t₀. V_(i,0) may be obtained through direct observation of therecent market values. Once V_(i,0) is known, it can be used as abaseline against which future values are computed.

V_(i,n) reflects the level of the model variable for item i at periodt_(n).

M_(i,n) represents incremental values of modifications to the baseconfiguration of item i of interest.

τ (tau) represents the locality adjustment coefficient where

$\begin{matrix}{\tau_{i,n} = \{ \begin{matrix}1 & {{{if}\mspace{14mu} t_{n}} = 0} \\0 & {otherwise}\end{matrix} } & \lbrack {{Equation}\mspace{14mu} 3} \rbrack\end{matrix}$

For example, τ=1 if t=0 (meaning for used values being observedcurrently) where BV_(i,0)(V_(i,0)+M_(i,n))×(τ_(i,n)×L_(p,n)) representsrecent market values vary by region (τ=1). In embodiments that do notforecast regional values, no locality adjustment is made (τ=0) forfuture values (forecast) if t>0.

L_(p,n) reflects the locality adjustment L_(p) made at time t_(n) to allitems in industry p (i ∈ p).

ΔF_(.,n|n−h) reflects the change in the macroeconomic (neitherindustry-specific nor item-specific) variable t_(n)-t₀, given thehistorical information about that variable in the last h=1, . . . , Hperiods (t_(n−1), t_(n−2), . . . , t_(n−H)).

ΔG_(p,n|n−h) reflects the change in the microeconomic variable t_(n)-t₀,given the historical information for industry p (i ∈ p) available aboutthat variable in the last h=1, . . . , H periods (t_(n−1), t_(n−2), . .. , t_(n−H)).

β₁ reflects the set of the coefficient(s) of the macroeconomic (neitherindustry-specific nor item-specific) variable, given the historicalinformation about that variable in the last h=1, . . . , H periods(t_(n−1), t_(n−2), . . . , t_(n−H)),

β₂ reflects the set of the coefficient(s) of the microeconomic variable,given the historical information for industry p (i ∈ p) available aboutthat variable in the last h=1, . . . , H periods (t_(n−1), t_(n−2), . .. , t_(n−H)),

_(iU,n|n*) reflects a competitive set adjustment made to item i based attime period t_(n*) based on an observed discrepancy between V_(i,n) andthe predicted values of all other items, j=1, . . . , J (i≠j) in thecompetitive set U (i, j ∈ U ∀ j) evaluated at some reference period,t_(n).

BV_(i,0) represents the baseline value of item i at t=0, adjusted formodifications M_(i,n) and locality L_(p,n).

The output (V_(i,h)) of Equation 2.1 or 2.2 represents an h-monthhistorical average current market value expressed in t_(0−n) monthshistorical average, reflecting the market information across alllocalities, Z, in which item i is available.

If a baseline value cannot be determined or obtained directly for itemi, the system may construct K competitive sets, U_(k), of similar and/orsubstitute items in the same industry and select the most similar item j(i≠j) as a substitute (see Equation 6) and use its value.

If the substitute item j's value was constructed in a time period beforet₀, the system may escalate the value based on inflation values forindustry p in which items i and j are assigned.

As discussed above, the farther away in time a forecast is relative tothe baseline value, the more uncertainty will exist and the moreforecasting error ε_(n,0) may exist. This seemingly unavoidable natureof forecasting future residual values can be highly undesirable, if notdetrimental, to certain entities that rely on knowledge of the futureresidual values to make important decisions, sometimes with severeconsequences, if the forecasted residual values are less than accurate.For example, knowledge of the future residual values may be useful tosome entities some entities in setting leasing rates which reflect theexpected change in valuation between the beginning and ends of a fixedlease period. As another example, knowledge of the future residualvalues may be useful to some entities in determining the amount at whichan item can be resold at any time period—a useful metric that can beused in investment decisions such as real estate. As yet anotherexample, knowledge of the future residual values may be useful to someentities in providing information supporting the strategic planningdecisions made of the manufacturer of item i.

Furthermore, knowledge of the future residual values may be useful inunderstanding and determining whether the change in value will beconstant over time intervals of the same length. For example, returningbriefly to FIG. 8, the change (ΔV_(i,(1,0))) between V_(i) at t₀(represented by V0 in FIG. 8) and V_(i) at t₁ (represented by V1 in FIG.8) is larger than the change (ΔV_(i,(2,1))) between V_(i) at t₂(represented by V2 in FIG. 8) and V_(i) at t₁ (represented by V1 in FIG.8). Constant changes in valuation over all periods of equal length,Δ_((n,m))=Δ_((m,p)) (m≠p) would result in a function between time andvalue represented by a straight line (increasing, decreasing or flat)while non-constant changes would be represented by a non-linearfunction.

To understand the relationship between residual values and time,embodiments employ both historical and current data. For example, ifthere is an underlying monthly seasonality in the residual values overtime, it would take a few years of historical data to be able to detect,measure, or estimate the amount of seasonal variation. Additionally, itwould be difficult to forecast residual values for an interval Δ_((n,m))if the historical data used to construct the forecasting model has alength Δ<Δ_((n,m)). An additional data constraint results from thefrequency at which the data used to construct the model (e.g.,macroeconomic, microeconomic, competitive sets, etc.) is updated. Ifeach of r=1, . . . , R input variables (not to be confused with the Qand R notations explained below) has an update frequency of φ_(r), thenthe frequency at which the residual forecasts can be updates is

$\phi^{*} = {\min\limits_{r}{( \phi_{r} ).}}$

The knowledge of whether a residual value curve is linear or non-linearmay be deterministic as to how the effect of potential time degradationis handled. For example, although the first observation (the time periodwhen item i first becomes available on the market) is indexed at t₀, insome cases, a user of the residual forecast relationship may beinterested in using a later time period, t_(s)≥t₀, as a starting pointfrom which changes in valuation are assessed—for instance, if item iwill not be purchased until t_(s) and will remain in the seller'sinventory until then. The anchor point for the curve remains fixed att₀, but the evaluation of the curve shift from Δ_((n,0)) to Δ_((n+s,s))(on the horizontal axis) and from ΔV_(i,h) to ΔV_(i,(n+s,s)) (on thevertical axis). If the residual value curve was linear, the shifting ofthe time evaluation window by s periods would have no impact on thevalue change. However, in the cases where the residual value curve isnon-linear, the appropriate time starting point should be chosen toaccount for the time degradation effect that occurs as item i remains inits original state.

Although the baseline value (V_(i,h)) of item i is known and remainsunchanged, the forecast of residual value of item i needn't also remainfixed over time. As new information becomes available that is reflectedin the variable types discussed above (e.g., variables in Equation 2.1or 2.2 representing data types 161-166), it is possible to employ thatadditional information to update the forecasted residual value of itemi.

Accordingly, in some embodiments, the system may operate to determine,at time t₀=0, a reference period, t_(n*), at which adjustments are to bemade to item i to align the baseline value of item i with values ofother items in a competitive set of similar and substitute items in thesame industry p as item i (503).

The reference period, t_(n*), may be determined in consideration of thefollowing constraints:

-   -   The minimum frequency in which the input data is updated. If        each of r=1, . . . , R input variables has an update frequency        of φ_(r), then the frequency at which the residual forecasts can        be updated is

$\phi^{*} = {\min\limits_{r}{( \phi_{r} ).}}$

The value of t_(n*) must be aligned with this frequency. For example, ifthe minimum frequency at which input data is updated on a monthly basis,the reference value, t_(max) must correspond to month-level temporaloffsets beyond t₀.

-   -   The expected total lifetime, t_(max), of item i. If item i is        not expected to retain value after period t_(max), then        t_(n*)≤t_(max).    -   The utility of the outputs (residual value forecasts) from the        residual value calculations. For example, if the forecasted        residual values are to be used for annual corporate strategic        planning, t_(n*) should also be based on an annual offset to t₀        (or as close as possible given the two previous, more binding        constraints).

As an example, suppose the initial time point is Jul. 9, 2012 and inputdata to the model is updated on a monthly basis, the reference periodcould then be Jul. 9, 2012 to Aug. 9, 2012, Jul. 9, 2012 to Sept. 9,2012, Jul. 9, 2012 to Oct. 9, 2012, etc. The reference period can befurther constrained by the total expected lifetime of the item. Forexample, if the item is not expected to retain value after five years,then the reference period can be Jul. 9, 2012 to Jul. 9, 2017, or less(in one or more monthly temporal offsets as constrained by the updatefrequency of the input data to the model).

Once the reference period is determined, a number of forecasts desiredbetween the initial time point and the reference period can bedetermined (505). The number of forecasts determines how often aforecast of the residual value of the item is to be generated. Startingfrom the initial time point, the time interval at which a forecast is tobe generated can be the same as, or more than, the update frequency ofthe input data to the model. In some embodiments, the system maydetermine a constant width of time intervals, Δ_((p,q)), at whichforecasts are to be generated for item i. The selection of Δ_((p,q ))can be determined by considering the following constraints:

-   -   It must be chosen such that (t_(n*)−t₀)/Δ_((p,q)) is a positive        integer.    -   It must be greater than or equal to

$\phi^{*} = {\min\limits_{r}{( \phi_{r} ).}}$

Following the above example in which the expected lifespan of the itemis five years, if it is assumed that the reference period is two years,there can be, for example, 23 forecasts, each of which is generated at afixed time interval of one month. If the time interval is selected to besix months, then four forecasts are generated.

With the time interval determined, the system may determine a localityadjustment (L_(p)) to item i (507). If the value of item i does not varyby geographic region (the value of item i is the same in industry pacross all localities at the initial time period), then no localityadjustment needs to be made. If the base value of items in industry p towhich item i is assigned varies by geographic region, the baseline valueof item i at the initial time point to may be adjusted by computing aratio between the average cost of items in the industry in a particularlocality at a certain time point t_(n) and the local cost of items inthe industry across all localities at the same time point t_(n).

In some embodiments, the system may determine locality adjustment,L_(p,n), to item i as follows:

$\begin{matrix}{L_{p,n} = \frac{L_{p,n}^{\prime}(z)}{L_{p,n}^{\prime}(Z)}} & \lbrack {{Equation}\mspace{14mu} 4} \rbrack\end{matrix}$

where L′_(p,n)(z) represents the average cost of items in industry p inlocality z at time t_(n), and L′_(p,n)(Z) represents the local cost ofitems in industry p across all localities (z ∈ Z) at time t_(n).

As an example, consumer price index can be utilized to determine thecost information on items in various industries relative to localities.As a specific example, this ratio may be determined for items availablein the United States by referring to the Consumer Price Index (CPI)provided on a monthly basis by the U.S. Bureau of Labor Statistics. Asanother example, Statistics Canada produces similar series for thatcountry. Skilled artisans appreciate that many economically-developedcountries have consumer price index figures that can be used to generatethe locality adjustments. Note that computation of L_(p,n) is normallyperformed at t₀ and when a value modification is made to account formodifications made to item i with the base configuration.

The system may collect and/or estimate incremental values ofmodifications, M_(i,n), to the base configuration of item i (509). Therecan be many types of modifications. One example type can bemodifications that are both observable at a particular time t_(n) andare expected to retain some value in future time period(s) after theparticular time t_(n). Another example type can be modifications thatare not observable and/or not expected to retain value after theparticular time t_(n). Equation 5 below illustrates two types ofmodifications:

-   -   Type A        m_(i,n) ^((a))        : represents modifications made to the base configuration of        item i at time t_(n) that are observable (tangible and        measurable) and are expected to retain some value in future time        periods after time period t_(n),    -   Type B        m_(i,n) ^((b))        : represents modifications made to the base configuration of        item i at time t_(n) that are not observable and/or not expected        to retain value after a modification is made at time t_(n).

$\begin{matrix}{M_{i,n} = \{ \begin{matrix}{M_{i,{n - 1}} + {L_{p,{n{(z)}}} \times ( {m_{i,n}^{(a)} + m_{i,n}^{(b)}} )}} & {\mspace{56mu} {{{if}\mspace{14mu} t_{n}} \neq {t_{0}\mspace{14mu} {and}\mspace{14mu} {the}\mspace{14mu} {modification}\mspace{14mu} {was}\mspace{14mu} {made}\mspace{14mu} {in}\mspace{14mu} t_{n}} > 0}} \\{( {m_{i,n}^{(a)} + m_{i,n}^{(b)}} )} & {\mspace{101mu} {{{if}\mspace{14mu} t_{n}} = {t_{0}\mspace{14mu} {and}\mspace{14mu} {the}\mspace{14mu} {modification}\mspace{14mu} {was}\mspace{14mu} {made}\mspace{14mu} {in}\mspace{14mu} t_{0}}}} \\{{M_{i,{n - 1}} - ( {L_{p,{n{(z)}}} \times m_{i,n}^{(b)}} )}\mspace{70mu}} & {{if}\mspace{14mu} t_{n}\mspace{14mu} {period}\mspace{14mu} {following}\mspace{14mu} {the}\mspace{14mu} {last}\mspace{14mu} {Type}\mspace{14mu} B\mspace{14mu} {last}\mspace{14mu} {modification}}\end{matrix} } & \lbrack {{Equation}\mspace{14mu} 5} \rbrack\end{matrix}$

By adjusting the base configuration's value to account formodifications, M_(i,n), and locality adjustments, L_(p,n), the systemmay determine BV_(i,n) (see Equation 6)—a locality-adjusted value ofitem i as modified (“modified item i”) at the forecast time t_(n) (511).An example of this process is illustrated in FIG. 10, which shows theeffects of modification adjustments and locality adjustments over time.

BV _(i,n)−(V _(i,h) +M _(i,n))×(τ_(n) ×L _(p,n))   [Equation 6]

In some embodiments, the system may construct competitive sets ofsimilar and/or substitute items in the same industry to which item ibelongs (513). This construction may involve partitioning all items inthe industry into k distinct clusters based on a measure of similaritybetween all pairs of items in the industry. A full explanation of anexample competitive set approach is provided in U.S. Pat. No. 8,661,403,issued Feb. 25, 2014, entitled “SYSTEM, METHOD AND COMPUTER PROGRAMPRODUCT FOR PREDICTING ITEM PREFERENCE USING REVENUE-WEIGHTEDCOLLABORATIVE FILTER,” which is fully incorporated herein by reference.Other competitive set approaches may also be possible.

As an example, a durable good, x_(i), can be described by its features(1, . . . , m) (also known as characteristics or variables) as follows:

x_(i)={X_(i,1), X_(i,2), . . . , X_(i,m)}

and all N distinct goods may be represented in matrix form as

$X = {\begin{bmatrix}x_{1,1} & x_{1,2} & \cdots & x_{1,{m - 1}} & x_{1,m} \\x_{2,1} & x_{2,2} & \cdots & x_{2,{m - 1}} & x_{2,m} \\\vdots & \vdots & \ddots & \vdots & \vdots \\x_{{N - 1},1} & x_{{N - 1},2} & \cdots & x_{{N - 1},{m - 1}} & x_{{N - 1},m} \\x_{N,1} & x_{N,2} & \cdots & x_{N,{m - 1}} & x_{N,m}\end{bmatrix}.}$

The similarity, s_(ij), between item i and item j based on a comparisonof Q observable features, can be computed using the Minkowski metric:

s _(ij)=1−[Σ_(q=1) ^(Q) W _(q) |x _(i,q) −x_(j,q)|^(λ)]^(1/λ)  [Equation 7]

where λ≥0, 0≤s_(ij)23 1, and Σ_(q=1) ^(Q)w_(q)=1. Note that although theformat of the data for some options (e.g., original equipmentmanufacturer options, dealer-installed vehicle options, etc.) may not benumeric, similarity can still be established across features by firsttransforming the data to a numeric scale. Programming techniquesnecessary to perform such a data transformation (e.g., text mining totransform text strings to numerical fields) are known to those skilledin the art and thus are not further described herein.

At time period t_(n), the system may compute the similarity for everypair of the N observations in the data set X, X_(i)≠X_(j), and then aN×N matrix of similarities, S_(n). There isn't a need to compute thevalues of s_(ii) since the similarity between an observation and itselfis, by definition, 1. With a subtraction from an N×N identity matrix,the dissimilarities can be computed (S _(n)=1−S_(n)) and used to buildclusters, at time t_(n), comprising K distinct competitive sets, U_(k,n)(k=1, . . . , K). Using S _(n), the system can employ any one of avariety of hierarchical clustering methods to partition the observationsinto distinct competitive sets. Examples of hierarchical clusteringmethods can be found in A. D. Gordon, CLASSIFICATION, 1999. When thenumber of observations, N, is large, the system may employ the K-meansclustering method after reprojecting S _(n) into an Q-dimensional set ofpoints on a scale that preserves the dissimilarities that are invariantto translation and rotation. The mechanics of the K-means clusteringmethod below can be found in Hartigan, J. A. and Wong, M. A., “A K-meansClustering Algorithm,” Applied Statistics 28, 1979, pp. 100-108.

1) Decide on a value for K.

2) Define K cluster centers (randomly, if necessary).

3) Decide the class memberships of the N objects by assigning them tothe nearest cluster center.

4) Re-estimate the K cluster centers, by assuming the memberships foundabove are correct.

5) If none of the N objects changed membership in the last iteration,exit. Otherwise, go to 3).

As a specific example, if the K-means clustering method is employed, thesystem may partition the data into K clusters by maximizing thewithin-cluster variation. If a cluster is indexed by k containing n_(k)observations, the overall within cluster variance based on a clusteringoutcomes is:

$\begin{matrix}{\sigma_{w}^{2} = {\sum\limits_{k = 1}^{K}\; {\sum\limits_{q = 1}^{Q}\; {\sum\limits_{i = 1}^{n_{q}}\; ( {x_{{(k)}_{i,q}} - {\overset{\_}{x}}_{{(k)}_{\bullet,q}}} )^{2}}}}} & \lbrack {{Equation}\mspace{14mu} 8} \rbrack\end{matrix}$

And the overall variance of the clustering outcome is the sum of thewithin-cluster and between-cluster variances: σ²=σ_(w) ²+σ_(b) ².

Skilled artisans appreciate that a number of statistics may be utilizedto decide how many clusters are to use. As a specific example, theCalinski-Harabasz index may be used:

$\begin{matrix}\frac{\sigma_{b}^{2}/( {K - 1} )}{\sigma_{w}^{2}/( {N - K} )} & \lbrack {{Equation}\mspace{14mu} 9} \rbrack\end{matrix}$

At every time period, t_(n), since the variables used to computesimilarity may be time-dependent, the competitive set can be recomputed.At the end of this process, every item i, . . . , I will belong toone-and-only-one of the K competitive sets, U_(k,n).

To account for macroeconomic factor(s), the system may collectnon-industry-specific macroeconomic data, F_(.,n|n−h), and eitherforecast future levels or incorporate existing forecasts from othersources to determine a macroeconomic factor {circumflex over(F)}_(.,n|n−h) (515).

Here, “F.” implies that the macroeconomic factors are taken over allindustries and not specific to any particular industry p. “

” indicates that it is an estimated value.

The single-dimensional macroeconomic factor, {circumflex over(F)}_(.,n|n−h) can be represented by a linear combination of Qvariables, f_(.,(n|n−h),q)(q=1, . . . , Q) , where Q represents thenumber of macroeconomic features under consideration, for example,housing prices, gas prices, unemployment, the Dow Jones IndustrialAverage, etc., and q represents one single macroeconomic feature. If thecurrent time period is t_(m), the information regarding future periodst_(n)>t_(m), will need to be forecasted.

Additionally, the data source may be internally-derived by theorganization generating the residual value forecasts (and the value ofthe q^(th) variable at time t_(m) is denoted {circumflex over(f)}_(.,(m|m−h),q)). An example of an internally-derived data source isthe ALG economic index shown in FIGS. 11 and 12. In this disclosure, the“ALG economic index” refers to a proprietary statistical measure ofchanges in a representative group of individual data points derived byALG, Inc. of Santa Monica, Calif. The ALG economic index tracks currenteconomic health and can be driven, for example, by threecomponents—overall retail spending in the economy, employment ratio(e.g., how many people out of a working population are employed), andper capita gross domestic product (GDP). Alternatively, it may be froman external source such as an organization that provides economicanalysis/forecasting (and the value of the q^(th) variable at time t_(m)is denoted by {circumflex over (f)}′_(.,(m|m−h),q)).

When gathering the data from multiple sources, it becomes necessary tocombine them into a single value, f_(i,m,q). One method for combiningthese values is to create a single value which gives more weight to thedata source or data type in which higher confidence is held. Forexample, a competitive set is a collection of vehicle data (e.g., ModelYear, Make, Model, Trim, etc.) which are in a particular segment (e.g.,midsize sedans such as Honda Accord and Toyota Camry). A more completedata set with, for instance, good pricing information will providehigher confidence as the competitive set can more easily be determinedbased on, for example, reliable pricing data. For the purpose ofexplanation, the system may use item j and time t_(m), where item j isused to represent a suitable substitute for item i in the samecompetitive set. Or, i and j may be the same item if a sufficient amountof historical data is available. The subscript m is for the time periodas it may be possible that historical information required to estimatemodel parameters is only available from period t_(m−h) to t_(m) wheret_(n)≥t_(m).

Accordingly, the combining equation at time t_(m) for item j can beexpressed as follows:

f.,m,q=φm{circumflex over (f)} _(.,(m|m−h),q)+(1−φ_(n)){circumflex over(f)}′_(.,(m|m−h),q)0≤φ_(m)≤1   Equation 10]

where

$\begin{matrix}{\phi_{m} = {\frac{\tau_{m}^{2}}{\tau_{m}^{2} + \sigma_{m}^{2}}.}} & \lbrack {{Equation}\mspace{14mu} 11} \rbrack\end{matrix}$

Here, τ_(m) ² represents the squared estimation error for theexternally-derived variable, {circumflex over (f)}′_(.,(m|m−h),q)), andσ_(m) ² represents the squared estimation error for theexternally-derived variable, {circumflex over (f)}_(.,(m|m−h),q)). Afterall of the variables have been collected reflecting their values fromtime period t_(m−h) to t_(n), the relationship between these variablesand the modification/locality-adjusted base value, BV_(j,m), can beexpressed as a linear combination of input variables:

BV _(j,m)(f)=a _(o)+Σ_(q−1) ^(Q)α_(q f.,m,q)+ε_(j,m).   [Equation 12]

To determine the values of the Q+1 coefficients, α₀, . . . , α_(Q), thesystem may use the statistical method of Ordinary Least Square (OLS)regression as shown in Equation 13 below:

_(j,m (f))={circumflex over (α)}_(o)+Σ_(q=1) ^(Q){circumflex over(α)}_(q f.,m,q)   [Equation 13]

where the estimated values, {circumflex over (α)}_(o), . . . ,{circumflex over (α)}_(q), are chosen such that the sum of squarederrors for good j (SSEj) as shown in Equation 14 below is minimized.

SSE _(j)(f)=Σ_(m=0) ^(h≤n)(BV _(j,m (f))−

_(j,m (f)))².   [Equation 14]

The estimation of the linear coefficients, {circumflex over (α)}₀, . . ., {circumflex over (α)}_(Q), need not be computed at every period,rather the coefficients can be updated periodically, say at time t_(p),and then used to forecast the value of

_(j,m (f))={circumflex over (α)}_(o), . . . , {circumflex over (α)}_(Q),for any time period t_(m)≥t_(p).

As the final step, once the observed or forecasted values of f_(i,n,q)are determined, the macroeconomic factor for item i, {circumflex over(F)}_(i,n|n−h), can be estimated as shown in Equation 15 below.

$\begin{matrix}{{\hat{F}}_{i,{n{n - h}}} = {( \frac{{\hat{\alpha}}_{o} + {\sum\limits_{q = 1}^{Q}\; {{\hat{\alpha}}_{q}f_{.{,n,q}}}}}{{BV}_{i,n}} ).}} & \lbrack {{Equation}\mspace{14mu} 15} \rbrack\end{matrix}$

In some embodiments, the system may also collect industry-specificmicroeconomic data, G_(p,n|n−h), for industry p in which item i beingevaluated is classified and determine a microeconomic factor,g_(p,n|n−h) (517). The single-dimensional microeconomic factor for itemi, Ĝ_(i,n|n−h), can be represented by a linear combination of Qvariables, g_(p(n|n−h),q)(q=1, . . . , Q) for industry trends,industry-specific inventories/supply, industry-specific demand, etc. Ifthe current time period is t_(m), the information regarding futureperiods t_(n)>t_(m), will need to be forecasted.

Additionally, the data source may be internally-derived by theorganization generating the residual value forecasts (and the value ofthe q^(th) variable at time t_(m) is denoted ĝ_(p,(m|m−h),q)).Alternatively, it may be from an external source such as an organizationthat provides economic analysis/forecasting (and the value of the q_(th)variable at time t_(m) is denoted by

_(p,(m|m−h),q)). For microeconomic features and macroeconomic features,q=1, . . . , Q variables have been denoted where F is the combination ofvariables (q=1, . . . Q) for macroeconomic features, f is a singledimensional macroeconomic variable, G is the combination of allmicroeconomic variables (q=1, . . . Q), and g is a final singledimensional microeconomic variable.

When gathering the data from multiple sources, it becomes necessary tocombine them into a single value, f_(p,m,q). As explained above, q=1, .. . Q is being used for both microeconomic and macroeconomic variables.That is, q refers to the amount of variables, and Q and R the amount ofbetas. r is used here to describe the various external forecastingsources of one factor q (e.g., segment supply) in g (i.e., r refers toweighting one factor within g (factor g=1, . . . Q) by various sourcesr1, r2, etc.) that can be combined to come up with q such thatBV_(j,m)(g)=β_(o)+Σ_(r=q) ^(Q)β_(q) g_(p,m,q)+∈_(j,m) (see Equation 18).In this way, one variable can reflect the data from multiple sources. Asdescribed above, one method for combining these values is to create asingle value which gives more weight to the data source in which higherconfidence is held. Following the above example notation (Equation 15),the combining equation at time t_(m) for item j can be:

ĝ _(p,m,q)=γmĝ_((m|m−h),q)+(1−γ_(m))ĝ′_(p,(m|m−h),q)0≤γ_(m)≤1  [Equation 16]

where

$\begin{matrix}{\gamma_{m} = \frac{\tau_{m}^{2}}{\tau_{m}^{2} + \sigma_{m}^{2}}} & \lbrack {{Equation}\mspace{14mu} 17} \rbrack\end{matrix}$

Here, τ_(m) ² represents the squared estimation error for theexternally-derived variable, ĝ′_(p,(m|m−h),q)), and σ_(m) ², representsthe squared estimation error for the externally-derived variable,ĝ_(p,(m|m−h),q)). “Externally” in this case means that they (e.g.,external sources/variables) are outside of the equation and are notdetermined by the equation. After all of the variables have beencollected reflecting their values from time period t_(m−h), to t_(n),the relationship between these variables and themodification/locality-adjusted base value, BV_(j,m), can be expressed asa linear combination of input variables as follows:

BV _(j,m)(g)=β_(o)+Σ_(r=q) ^(Q)β_(q)g_(p,m,q)+ε_(j,m)   [Equation 18]

To determine the values of the Q+1 coefficients, β_(o), . . , β_(Q), thesystem may use the statistical method of OLS regression as shown inEquation 19:

_(j,m)(g)={circumflex over (α)}_(o)+Σ_(q=1) ^(RQ){circumflex over(β)}_(p) g _(p,m,q)   [Equation 19]

where the estimated values, {circumflex over (α)}_(o), . . , {circumflexover (β)}_(Q.) are chosen such that the sum of squared errors for item j(SSEj) as shown in Equation 20 is minimized.

SSE _(j)(f)=Σ_(m=0) ^(h≤n)((BV _(j,m)(g)−

_(j,m)(g))²   [Equation 20]

The estimation of the linear coefficients {circumflex over (β)}_(o), . .. , {circumflex over (β)}_(Q), need not be computed at every period.Rather, the coefficients can be updated periodically, for instance, attime t_(p), and then used to forecast the value of

_(j,m)(f)={circumflex over (β)}_(o), . . . {circumflex over (β)}_(Q). .. , for any time period t_(m)≥t_(p).

As the final step, once the observed or forecasted values of g_(p,n,q)are determined, the microeconomic factor can be estimated as shown inEquation 21 below.

$\begin{matrix}{{\hat{G}}_{i,{n{n - h}}} = {{( \frac{{\hat{\beta}}_{o} + {\sum\limits_{r = 1}^{R}\; {{\hat{\beta}}_{r}g_{p,n,q}}}}{{BV}_{i,n}} )\mspace{14mu} {where}\mspace{14mu} i} \in {p.}}} & \lbrack {{Equation}\mspace{14mu} 21} \rbrack\end{matrix}$

With all the pieces assembled, the system can generate a residual valuefor time t_(n) for items i (519). As an example, this can beaccomplished by substituting the values constructed in earlier stepsinto Equation 22 below.

{circumflex over (V)} _(i,h) =BV _(i,0)+((Σ_(q=1) ^(Q){circumflex over(α)}_(q, f, n,q))({circumflex over (F)}_(n|n−h) −F _(t0))+(Σ_(r=1)^(QR){circumflex over (β)}_(r) g_(p,n,q))(Ĝ_(p,n|n−h) −G _(t0))) h=1, .. . , H   [Equation 22]

Equation 22 and its computational components (with their correspondingdriving factors) are illustrated in FIG. 11 which depicts a plot diagramillustrating percentage points adjustments by factor, according to someembodiments disclosed herein. In the example of FIG. 11, the firstcomputational component ({circumflex over (V)}_(i,h)) is driven by thebaseline value of the item of interest (over a historical average), thesecond computational component ((Σ_(q=1) ^(Q){circumflex over(α)}_(q f.,n,q))({circumflex over (F)}_(n|n−h)F_(t0))) is driven by anumber of macroeconomic factors, such as gas, ALG economic index, andindustry supply), and the third computational component ((Σ_(r−1)^(QR){circumflex over (β)}_(r) g_(p,n,q))(Ĝ_(p,n|n−h)−G_(t0))) is drivenby a number of microeconomic factors such as segment supply (e.g., thesupply level of a used vehicle market segment of interest), model supply(e.g., the supply level of a used vehicle model of interest), incentivespending (e.g., incentives offered by the vehicle manufacturer of thevehicle model of interest), rental fleet penetration, redesign, and ALGbrand outlook or value. Rental fleet penetration reflects a percentageof new cars entering the rental fleet. For instance, 2,000 of 20,000 newcars sold in a month to a rental company means a 10% rental fleetpenetration. Redesign refers to vehicle updates such as a complete newgeneration (e.g., a complete new model), minor updates (e.g., frontenddesign changes), or major updates (e.g., interior changes, newpowertrain, etc.). Brand outlook or value refers to a measure used by abrand to determine a level of brand trending. Brand outlook can bemeasured statistically in the used transaction data where the brand rankorder in the data can be identified. Consumer surveys can also be usedto rank brands.

Optionally, residual value forecasting method 500 may further compriseperforming one or more quality assurance (QA) operations on thegenerated output (521). In some embodiments, the system may compare theforecasted values with a set of reference values. The time point atwhich the forecasted residual curve is aligned occurs at t*_(n) selectedpreviously (see 505). The approach for adjusting {circumflex over(V)}_(i,n) for QA purposes may include the following steps:

a. Gather residual values from other vehicles in the competitive set(see FIG. 9, which shows a final adjustment based on competitive setcomparison). These may include:

-   -   The average residual value at t*_(n) for the entire competitive        set U_(k);    -   The baseline value BV_(j,0) at to for item j in the same        competitive set that is most similar (best match) to item i; and    -   The residual value of item k that is a previous version of item        i (not a modification of item i, but the one that was replaced        in production by item i), if it exists.

b. Compute the adjustment value, C_(iU,n|n*), that will minimizes theweighted average error relative to the position implied by the referencepoints as shown in Equation 23 below:

C _(iU,n|n*)=Δ[(αV_(U,n) +βBV _(j,0) +ΓV _(k,n))−V _(i,n)]  [Equation23]

where α, β, Γ are assigned weights, α+βΓ=1 and α,β,Γ>0, Δ is a weightdepending on whether item i at t_(n) is completely new in the market(Δ=1) or established (Δ<1).

If necessary, the system may adjust the output from Equation 22 with theoutput from Equation 23 to generate a final forecast of residual valueof the item (523). As an example, the system may adjust {circumflex over(V)}_(i,n) by C_(iU,n|n*) to get the final forecasted value:

{circumflex over (V)} _(i,h) =BV _(i,0)+((Σ_(q−1) ^(Q){circumflex over(α)}_(q f.,n,q))({circumflex over (F)} _(n|n−h) −F _(t0))+(Σ_(r−1)^(R){circumflex over (β)}_(r) g _(p,n,q)(Ĝ _(p,n|n−h) −G_(t0)+C_(iU,n|n*) h=1, . . . ,H   [Equation 24]

Referring to FIG. 9, an exemplary residual curve adjustment is shown. InFIG. 9, the dotted line (900) represents the initial computation of thecurve. Points A, B, and C represent the average residual values of thecompetitive set, the current market value of the best matching item inthe competitive set, and the previous value of the item of interest,respectively. Taking these data points into account, the final revisedresidual curve is shown as line 910.

FIG. 6 is a flow diagram illustrating an example of a method foroptionally revising a generated residual value curve based onqualitative input via a feedback cycle, according to some embodimentsdisclosed herein.

In some embodiments, after a baseline residual curve has been generatedand stored in a local data storage device, a user of the enterprisecomputing environment can provide editorial input (605) that is used torevise the residual curve (610). The editorial input may be provided toaccount for any factors that were not accounted for in the generation ofthe baseline curve, or that have changed since the baseline curve wasgenerated. The editorial input may also be provided to determine thepotential effect of various factors on the residual curve. The editorialinput may be provided through a workbench application (see, e.g., FIG.12) that allows the user to see the results of the input. The residualcurve that is revised according to the editorial input can then be made“live” (615). In other words, the revised residual curve can be storedor published to a location to which client access can be enabled. Thesystem allows for periodic revision of the residual curve. If it is timeto do so (620) (e.g., if a predetermined interval has been reached), theuser can provide additional editorial input (605) for generation of anewly revised residual curve (610), which can then be published foraccess by the client (615).

In one embodiment, the residual curve is updated at regular intervals.The updated residual curve can be stored in place of the previousbaseline curve and used as the baseline for future use. When theresidual curve is updated, several comparisons are made to ensure thatthe newly revised curve is reasonable. For example, the revised curve iscompared to the previous curve to determine whether the values of thenew curve differ from the previous curve by a substantial amount. If thedifference is too great, this may indicate that the inputs to therevised curve are not realistic. The inputs may therefore be adjusted tobring the revised residual curve closer to the previous curve. In oneembodiment, the residual curve is also adjusted based on the currentvalues of items in a competitive set. For instance, the curve may beadjusted to bring the curve closer to the value of a closest competitiveitem, or to the average value of the set of competitive items.

FIG. 7 is a flow diagram illustrating an example of a method foroptionally allowing a client to provide qualitative input on a generatedresidual value curve, according to some embodiments disclosed herein.

In some embodiments, after a baseline residual curve is revised, theserver may enable access by a client to the revised curve (705).Customers can access the residual curve through the client to determinethe value of the item at some point in the future. The client in thisembodiment includes a workbench application that allows the customer tovary some of the factors that affect the residual curve and to view theresulting changes to the residual curve. The server receives input fromthe client's workbench application (710) and revises the residual curveaccording to the received input (715). The newly revised residual curveis then provided to the client (720) so that it can be viewed by thecustomer.

Example Implementation in Automotive Industry

The following describes an exemplary implementation in which theapproach described above is adapted to be used to estimate the value ofautomobiles over time and thereby allows the resale values that could beexpected at future time points to be determined. Residual values thusestimated can provide guidelines for pricing fixed-term vehicle leaseswhich captures the expected change in value that will result in the timeinterval between the leased vehicle's acquisition at time to and itsdisposition at time t_(d). This example implementation not only canprovide the estimated residual value at disposition, V_(i,d), but canalso forecast values at equally-spaced fixed points between t₀ andt_(d), thereby allowing construction of a residual curve that capturesthe relationship between vehicle value and time. Over time and as newinformation becomes available, this example implementation continues totimely reflect changing values of exogenous macroeconomic andindustry-specific microeconomic variables and vehicle-specific,endogenous variables (depreciation, competitive sets, andmodifications).

In this example implementation, the guidelines for production of theresidual values include:

-   -   The residual values must be realistic and adjusted over the        vehicle's lifecycle to reflect the market, incentives and fleet        purchases.    -   To enable vehicle manufacturers to market their vehicles,        residual values must create clear, consistent messages across        all vehicles being valued.    -   To remain relevant and timely, the residual values must reflect        product enhancements, packaging/content adjustments, etc.    -   To provide utility to each manufacturer's ecosystem, the        residual values must encompass all phases of the automotive        sales cycle, including dealer engagement, manufacturer support,        cooperation on pricing, and off lease supply management.

In this example, the methodology described above is adapted to estimateresidual values of cars and light trucks in the United States andCanada. Estimates are updated every two months to reflect new observeddata, market conditions, and macroeconomic estimates. As an example ofthis embodiment, the latest 2017 Model Year (MY) Hyundai Elantra SE withautomatic transmission (AT)—which sells at popular equipped MSRP of$19,785 in California—will be used (see FIG. 4 for an image). Thisparticular model has some historical used market value and residualvalue data available to estimate the future value of the vehicle.Furthermore, exogenous macroeconomic data and microeconomic data as wellas endogenous factors (e.g., depreciation rate and competitiveknowledge) are readily available to construct the current residual valuecurve for this vehicle at any term (e.g., 12-month, 24-month, 36-month,. . . , 60-month or any term in between).

Step 1. Determine a baseline, unmodified value for the item i, V_(i,h).The 2014 MY Hyundai Elantra SE AT (item i) baseline value for t₀ isV_(i,h)=$11,075 and is based on an observed current market value (CMV)derived from auction data. Roughly 990 auction records were available inan h estimation period for item i to create the CMV of $11,078 byapplying statistical filters and other measures to cleanse the data. Forthe purpose of illustration and not of limitation, auction records mayinclude such information as: Sale Date; National Automobile DealersAssociation (NADA) Vehicle Identification Code; Make; Sub-make; ModelYear; Series; Body Style; Diesel 4WD Identifier; NADA Region code; SalePrice; Mileage; Sale Type; Vehicle Identification Number (VIN); VehicleIdentifier (VI D); etc.

Step 2. At time t_(n)=0, determine a reference period, t_(n*), at whichadjustments will be made to align values with other items in thecompetitive set based on the following industry-level frequencies thatconstrain the choice of t_(n*):

-   -   auction data is updated weekly yet also aggregated to monthly        numbers, while microeconomic factors and macroeconomic factors        are updated monthly;    -   forecasted terms go up to 72-month, t_(max) is greater than        72-month;    -   most common terms are 12, 24, 36, 48, and 60-month terms and,        mostly, 36-month is used.

Because a 36-month alignment is commonly used in the automotiveindustry, a value of t_(n*)=36 months is used in this example for thereference period relative to the baseline.

Step 3. At time t_(n)=0, determine the constant width of time intervalsΔ_((p,q)) at which forecasts will be generated. In this case, theselection of Δ_((p,q)) is determined by considering the followingconstraints:

-   -   It must be chosen such that (t_(n*)−t₀)/Δ_((p,q)) is a positive        integer where t_(n*)−t₀=36 months.

It must be greater than or equal to

$\phi^{*} = {{\min\limits_{r}( \phi_{r} )} = {{weekly}\mspace{14mu} {since}}}$

that is the frequency at which the macroeconomic data is updated.

Given those constraints and a choice of t_(n*)=36, the Δ_((p,q))=2months is used.

-   -   36-month term/2 month=18>0.    -   Interval is greater than φ* (weekly data).

Step 4. Determine a locality adjustment, L_(p). If the base value of theitems in industry p to which item i is assigned varies by geographicregion, then compute

$L_{p,n} = \frac{L_{p,n}^{\prime}(z)}{L_{p,n}^{\prime}(Z)}$

where L′_(p,n)(z) is the average cost of items in industry p in localityz at time t_(n), and L′_(p,n)(Z) is the local cost of items in industryp across all localities (z ∈ Z) at time t_(n). In this example, theresidual value of the 2017MY Hyundai Elantra SE AT is being establishedfor California, located in the z=“US West” region of the U.S. And whereL_(west)=1/1, local adjustment for U.S. Western region is 100% of theaverage for all regions in the U.S.

Step 5. Collect or estimate incremental values of modifications,M_(i,n), to the base configuration of the item. In this example, thevehicle has cruise control added as popularly equipped which retains ameasurable and tangible value of $375 at 36-month. Thus,M_(Elantra,36-month)=$375 for all regions,M_(Elantra,36-month)+($375*1.0). For U.S. Western region.

Step 6. Determine the locality-adjusted value of the modified item i attime to by adjusting the base configuration's value to account formodifications and locality adjustments.

BV _(i,n)=(V′ _(i,0) +M _(i,n))×(τ_(i,n) ×L_(p,n))=($11,075+$375)*1.0=$11,450

Step 7. Construct competitive sets, C_(iW,n), of similar and substituteitems in the same industry, p. This involves determining what factors tocompare to for each competitor and establishing a matrix such as pricing(e.g., MSRP), engine and performance (e.g., horse power, mile pergallon, torque, displacement, etc.), exterior (e.g., curb weight,wheelbase, length, width, height, wheels size, etc.), interior (e.g.,dimensions, features, air conditioning, entertainment system, seats,etc.) And safety.

Based on the factors above and the matrix analysis, for example, the2014 Honda Civic LX AT has the most similarities to the 2014 Hyundai SEAT, followed by 2014 Toyota Corolla L AT.

Step 8. Collect macroeconomic data, F_(.,n|n−h), and either forecastfuture levels or incorporate existing forecasts from other sources todetermine {circumflex over (F)}_(.,n|n−1).

As an example, suppose the ALG economic index, industry-wide used marketsupply index, and gas prices are collected at to and forecasted fort_(n) (see, e.g., FIG. 11). Further suppose the ALG economic index isequal to 100 index points, industry-wide used market supply index isequal to 100 index points, and average gas prices are $2.09 per gallonin t₀, whereas the forecasts are 111 points for the ALG economic index,123 points for industry wide used market supply, and $2.67 for gas pricein t_(36-month). The various factors have coefficients which determinedbased on correlation to auction data and thus the impact on theforecasted values can be applied by using the coefficients. Hence, forexample, based on the change in the ALG economic index from currently100 to 111 in 36-month, the impact on 36-month residual values is anincremental $60, from industry wide used market supply −$450, and fromgas prices $165. The total adjustment for macroeconomic variables is−$225 or, mathematically, +a1*(111-100)−a2*(11.5-11.0million)+a3*($3.00-$3.50)=$60-$450+$165=−$225.

Step 9. Collect microeconomic data, G_(p,n|n−h), for the industry inwhich the item being evaluated is classified and forecast future levelsor incorporate existing forecasts from other sources to determineĜ_(p,n|n−h).

Microeconomic data such as segment-level and model-level used vehiclemarket supply, brand value, incentive spending, and rental fleetpenetration is generated for t₀ and forecasted for t_(36-month). Forexample, current incentive spending for the Elantra is $2,600, yet theforecast is expected to be at $2,550. Based on the change in incentivespending from today to 36-month, the impact is $15, brand value $100,and used vehicle market supply (at the segment level and the modellevel) −$160 (see Equations 16-21). The total adjustment formicroeconomic variables is −$45, or mathematically,−b1*($1,900-$2,000)−b2*(20-18 indexpoints)−b3*(100-120)=$15+$100-$160=−$45.

Step 10. With all the pieces assembled, forecasting the residual valuefor time t_(n*)=36 month for the 2017 Hyundai Elantra SE AT (cruisecontrol, in California) can be done by substituting the valuesconstructed in earlier steps into Equation 22:

$\begin{matrix}{{\hat{V}}_{i,h} = {{BV}_{i,0} + ( {{( {\sum\limits_{q = 1}^{Q}\; {{\hat{\alpha}}_{q}f_{.{,n,q}}}} )( {{\hat{F}}_{n{n - h}} - F_{t\; 0}} )} + {( {\sum\limits_{r = 1}^{R}\; {{\hat{\beta}}_{r}g_{p,n,q}}} )( {{\hat{G}}_{p,{n{n - h}}} - G_{t\; 0}} )}} )}} \\{= {{( {{{\$ 11}\text{,}075} + {\$ 375}} )*1.0} + ( {( {- {\$ 225}} ) + ( {- {\$ 45}} )} )}} \\{= {{{\$ 11}\text{,}450} - {\$ 270}}} \\{= {{\$ 11}\text{,}180}}\end{matrix}$

Step 11. Perform quality assurance (QA). In this example, this involvescomputing the adjustment value, C_(iU,n|n*) (see Equation 23) that willminimizes the weighted average error relative to the position implied bythe reference points.

In this example, adjustment value in the case of the Camry LE AT issmall since Δ<1, plenty of history is available. The average residualvalue of entire competitive set is $10,840 and the following factors aretaken into account:

-   -   i. Baseline of the closest competitor(s) is $11,465    -   ii. 2014 MY Hyundai Elantra SE AT is $11,120

Applying Equation 23 described above, the adjustment value C_(iU,n|n*)is then equal to:0.25×((0.33×$10,840+0.33×$11,120+0.33×$11,465)−$11,180)=−$38

Step 12. Adjust {circumflex over (V)}_(i,h) by C_(iU,n|n*) to determinethe final forecasted value:

In this example, applying Equation 24 described above, the finalforecast for the 2017 MY Hyundai Elantra SE AT for the Western regionfor time t_(n*)=36 month is $11,180-$38=$11,142.

Embodiments disclosed herein can provide many advantages. For example,knowledge of the future residual values can be used to:

-   -   a) Set leasing rates of an item which reflect the expected        change in valuation of the item between the beginning and ends        of a fixed lease period a useful metric that can be used in the        rental industry.    -   b) Determine the amount at which an item can be resold at any        time period a useful metric that can be used in investment        decisions such as real estate.    -   c) Provide information supporting the strategic planning        decisions made of the manufacturer of item i.    -   d) Determine if the change in value will be constant over time        intervals of the same length.

These, and other, aspects of the disclosure and various features andadvantageous details thereof are explained more fully with reference tothe exemplary, and therefore non-limiting, embodiments illustrated anddetailed in this disclosure. It should be understood, however, that thedetailed description and the specific examples, while indicating thepreferred embodiments, are given by way of illustration only and not byway of limitation. Descriptions of known programming techniques,computer software, hardware, operating platforms and protocols may beomitted so as not to unnecessarily obscure the disclosure in detail.Various substitutions, modifications, additions and/or rearrangementswithin the spirit and/or scope of the underlying inventive concept willbecome apparent to those skilled in the art from this disclosure.

Embodiments discussed herein can be implemented in a computercommunicatively connected to a network (for example, the Internet),another computer, or in a standalone computer. As is known to thoseskilled in the art, a suitable computer can include a central processingunit (“CPU”), at least one read-only memory (“ROM”), at least one randomaccess memory (“RAM”), at least one hard drive (“HD”), and one or moreinput/output (“I/O”) device(s). The I/O devices can include a keyboard,monitor, printer, electronic pointing device (for example, mouse,trackball, stylist, touch pad, etc.), or the like. In embodiments of theinvention, the computer has access to at least one database over anetwork connection.

ROM, RAM, and HD are computer memories for storing computer-executableinstructions executable by the CPU or capable of being complied orinterpreted to be executable by the CPU. Suitable computer-executableinstructions may reside on a computer readable medium (e.g., ROM, RAM,and/or HD), hardware circuitry or the like, or any combination thereof.Within this disclosure, the term “computer readable medium” or is notlimited to ROM, RAM, and HD and can include any type of data storagemedium that can be read by a processor. Examples of computer-readablestorage media can include, but are not limited to, volatile andnon-volatile computer memories and storage devices such as random accessmemories, read-only memories, hard drives, data cartridges, directaccess storage device arrays, magnetic tapes, floppy diskettes, flashmemory drives, optical data storage devices, compact-disc read-onlymemories, and other appropriate computer memories and data storagedevices. Thus, a computer-readable medium may refer to a data cartridge,a data backup magnetic tape, a floppy diskette, a flash memory drive, anoptical data storage drive, a CD-ROM, ROM, RAM, HD, or the like.

The processes described herein may be implemented in suitablecomputer-executable instructions that may reside on a computer readablemedium (for example, a disk, CD-ROM, a memory, etc.). Alternatively, thecomputer-executable instructions may be stored as software codecomponents on a direct access storage device array, magnetic tape,floppy diskette, optical storage device, or other appropriatecomputer-readable medium or storage device.

Any suitable programming language can be used to implement the routines,methods or programs of embodiments of the invention described herein,including C, C++, Java, JavaScript, HTML, or any other programming orscripting code, etc. Other software/hardware/network architectures maybe used. For example, the functions of the disclosed embodiments may beimplemented on one computer or shared/distributed among two or morecomputers in or across a network.

Communications between computers implementing embodiments can beaccomplished using any electronic, optical, radio frequency signals, orother suitable methods and tools of communication in compliance withknown network protocols.

Different programming techniques can be employed such as procedural orobject oriented. Any particular routine can execute on a single computerprocessing device or multiple computer processing devices, a singlecomputer processor or multiple computer processors. Data may be storedin a single storage medium or distributed through multiple storagemediums, and may reside in a single database or multiple databases (orother data storage techniques). Although the steps, operations, orcomputations may be presented in a specific order, this order may bechanged in different embodiments. In some embodiments, to the extentmultiple steps are shown as sequential in this specification, somecombination of such steps in alternative embodiments may be performed atthe same time. The sequence of operations described herein can beinterrupted, suspended, or otherwise controlled by another process, suchas an operating system, kernel, etc. The routines can operate in anoperating system environment or as stand-alone routines. Functions,routines, methods, steps and operations described herein can beperformed in hardware, software embodied on hardware, firmware or anycombination thereof.

Embodiments described herein can be implemented in the form of controllogic in hardware or a combination of software and hardware. The controllogic may be stored in an information storage medium, such as acomputer-readable medium, as a plurality of instructions adapted todirect an information processing device to perform a set of stepsdisclosed in the various embodiments. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the invention.

It is also within the spirit and scope of the invention to implement insoftware programming or code an of the steps, operations, methods,routines or portions thereof described herein, where such softwareprogramming or code can be stored in a computer-readable medium and canbe operated on by a processor to permit a computer to perform any of thesteps, operations, methods, routines or portions thereof describedherein. The invention may be implemented by using software programmingor code in one or more general purpose digital computers, by usingapplication specific integrated circuits, programmable logic devices,field programmable gate arrays, optical, chemical, biological, quantumor nanoengineered systems, components and mechanisms may be used. Ingeneral, the functions of the invention can be achieved by any means asis known in the art. For example, distributed, or networked systems,components and circuits can be used. In another example, communicationor transfer (or otherwise moving from one place to another) of data maybe wired, wireless, or by any other means.

A “computer-readable medium” may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, system ordevice. The computer readable medium can be, by way of example only butnot by limitation, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, system, device,propagation medium, or computer memory. Such computer-readable mediumshall generally be machine readable and include software programming orcode that can be human readable (e.g., source code) or machine readable(e.g., object code). Examples of non-transitory computer-readable mediacan include random access memories, read-only memories, hard drives,data cartridges, magnetic tapes, floppy diskettes, flash memory drives,optical data storage devices, compact-disc read-only memories, and otherappropriate computer memories and data storage devices. In anillustrative embodiment, some or all of the software components mayreside on a single server computer or on any combination of separateserver computers. As one skilled in the art can appreciate, a computerprogram product implementing an embodiment disclosed herein may compriseone or more non-transitory computer readable media storing computerinstructions translatable by one or more processors in a computingenvironment.

A “computer” or “processor” may include any hardware system, mechanismor component that processes data, signals or other information. Acomputer or processor can include a system with a general-purposecentral processing unit, multiple processing units, dedicated circuitryfor achieving functionality, or other systems. Processing need not belimited to a geographic location, or have temporal limitations. Forexample, a computer or processor can perform its functions in“real-time,” “offline,” in a “batch mode,” etc. Portions of processingcan be performed at different times and at different locations, bydifferent (or the same) processing systems.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having,” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,product, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, process, article, orapparatus.

Furthermore, the term “or” as used herein is generally intended to mean“and/or” unless otherwise indicated. For example, a condition A or B issatisfied by any one of the following: A is true (or present) and B isfalse (or not present), A is false (or not present) and B is true (orpresent), and both A and B are true (or present). As used herein,including the accompanying appendices, a term preceded by “a” or “an”(and “the” when antecedent basis is “a” or “an”) includes both singularand plural of such term, unless clearly indicated otherwise (i.e., thatthe reference “a” or “an” clearly indicates only the singular or onlythe plural). Also, as used in the description herein and in theaccompanying appendices, the meaning of “in” includes “in” and “on”unless the context clearly dictates otherwise.

Although the foregoing specification describes specific embodiments,numerous changes in the details of the embodiments disclosed herein andadditional embodiments will be apparent to, and may be made by, personsof ordinary skill in the art having reference to this disclosure. Inthis context, the specification and figures are to be regarded in anillustrative rather than a restrictive sense, and all such modificationsare intended to be included within the scope of this disclosure. Thescope of the present disclosure should be determined by the followingclaims and their legal equivalents.

What is claimed is:
 1. A method for generating forecasts of residualvalues of an item of interest in an industry, the method comprising:programmatically receiving or obtaining, by a system, used market data,non-industry-specific data, and industry-specific data from multipledata sources, the system having a processor and a non-transitorycomputer-readable medium; transforming, by the system, the used marketdata, the non-industry-specific data, and the industry-specific datainto data representations internal to the system; applying, by thesystem, the data representations of the used market data, thenon-industry-specific data, and the industry-specific data as input to aresidual value forecasting model, the residual value forecasting modelhaving a first computational component driven by a baseline value forthe item of interest, a second computational component driven bymacroeconomic factors not specific to the industry, and a thirdcomputational component driven by microeconomic factors specific to theindustry, the first computational component having a baseline valuevariable for representing the baseline value for the item of interestwith a base configuration at an initial time point, the secondcomputational component having a macroeconomic factor represented by alinear combination of macroeconomic variables that representmacroeconomic features under consideration for the item of interest, thethird computational component having a microeconomic factor representedby a linear combination of microeconomic variables that representmicroeconomic features specific to the industry, the applying producinga forecasted residual value for the item of interest at a future timepoint; and providing the forecasted residual value for the item ofinterest for presentation on a client device.
 2. The method according toclaim 1, further comprising: determining the baseline value for the itemof interest, the determining comprising deriving, from auction data, anobserved current market value of the item of interest with the baseconfiguration at the initial time point; determining, at the initialtime point, a reference period at which adjustments are to be made tothe item of interest for value alignment with items that compete withthe item of interest in the industry; determining, at the initial timepoint based at least on the initial time point and the reference period,a constant width of time intervals at which forecasts are to begenerated for the item of interest; determining a locality adjustment tothe item of interest, the locality adjustment determined at the initialtime point and at a time a value modification being made to account fora modification to the item of interest; collecting or determiningincremental values of modifications to the base configuration of theitem of interest; determining, based at least on the locality adjustmentand the incremental values of modifications to the base configuration ofthe item of interest, a locality-adjusted value of the item of interesthaving the modifications to the base configuration; constructingcompetitive sets of similar and substitute items in the industry, theconstructing comprising partitioning items that compete with the item ofinterest in the industry based on a measure of similarity between pairsof the items; determining the macroeconomic factor by computing thelinear combination of macroeconomic variables that represent themacroeconomic features under consideration for the item of interest,determining the microeconomic factor by computing a linear combinationof observed or forecasted values of the microeconomic variables thatrepresent the microeconomic features specific to the industry; andcomputing the first computational component, the second computationalcomponent, and the third computational component of the residual valueforecasting model utilizing the baseline value for the item of interest,the locality-adjusted value of the item of interest having themodifications to the base configuration, the macroeconomic factor, andthe microeconomic factor thus determined.
 3. The method according toclaim 2, further comprising: computing, utilizing a baseline value of aclosest competing item in the competitive sets, an adjustment value; andgenerating a final forecasted residual value for the item of interest byadjusting the forecasted residual value for the item of interest withthe adjustment value.
 4. The method according to claim 2, wherein themodifications to the base configuration of the item of interest comprisevalue-affecting changes to the item of interest at any time point. 5.The method according to claim 2, further comprising: combining valuesreceived or obtained from the multiple data sources into a single valuewhich gives more weight to a data source or data type in which a higherconfidence is held, the combining including computing a combiningequation at a time point in the reference period for a similar orsubstitute item that competes with the item of interest in the industry.6. The method according to claim 2, wherein the reference period is 36months and wherein the constant width of time intervals is two months.7. The method according to claim 1, wherein the second computationalcomponent further includes a coefficient for the macroeconomic factorand wherein the third computational component further includes acoefficient for the microeconomic factor.
 8. The method according toclaim 1, wherein the used market data comprises open auction data,closed auction data, and certified pre-owned data.
 9. The methodaccording to claim 1, wherein the non-industry-specific data comprisesat least one of inflation, unemployment rate, gas prices, an economicindex, interest rates, or industry-wide used market supply.
 10. Themethod according to claim 1, wherein the industry-specific datacomprises vehicle-specific data and wherein the vehicle-specific datacomprises modifications to the base configuration of the item ofinterest.
 11. A system for generating forecasts of residual values of anitem of interest in an industry, the system comprising: a processor; anon-transitory computer-readable medium; and stored instructionstranslatable by the processor to perform: programmatically receiving orobtaining used market data, non-industry-specific data, andindustry-specific data from multiple data sources; transforming the usedmarket data, the non-industry-specific data, and the industry-specificdata into data representations internal to the system; applying the datarepresentations of the used market data, the non-industry-specific data,and the industry-specific data as input to a residual value forecastingmodel, the residual value forecasting model having a first computationalcomponent driven by a baseline value for the item of interest, a secondcomputational component driven by macroeconomic factors not specific tothe industry, and a third computational component driven bymicroeconomic factors specific to the industry, the first computationalcomponent having a baseline value variable for representing the baselinevalue for the item of interest with a base configuration at an initialtime point, the second computational component having a macroeconomicfactor represented by a linear combination of macroeconomic variablesthat represent macroeconomic features under consideration for the itemof interest, the third computational component having a microeconomicfactor represented by a linear combination of microeconomic variablesthat represent microeconomic features specific to the industry, theapplying producing a forecasted residual value for the item of interestat a future time point; and providing the forecasted residual value forthe item of interest for presentation on a client device.
 12. The systemof claim 11, wherein the stored instructions are further translatable bythe processor to perform: determining the baseline value for the item ofinterest, the determining comprising deriving, from auction data, anobserved current market value of the item of interest with the baseconfiguration at the initial time point; determining, at the initialtime point, a reference period at which adjustments are to be made tothe item of interest for value alignment with items that compete withthe item of interest in the industry; determining, at the initial timepoint based at least on the initial time point and the reference period,a constant width of time intervals at which forecasts are to begenerated for the item of interest; determining a locality adjustment tothe item of interest, the locality adjustment determined at the initialtime point and at a time a value modification being made to account fora modification to the item of interest; collecting or determiningincremental values of modifications to the base configuration of theitem of interest; determining, based at least on the locality adjustmentand the incremental values of modifications to the base configuration ofthe item of interest, a locality-adjusted value of the item of interesthaving the modifications to the base configuration; constructingcompetitive sets of similar and substitute items in the industry, theconstructing comprising partitioning items that compete with the item ofinterest in the industry based on a measure of similarity between pairsof the items; determining the macroeconomic factor by computing thelinear combination of macroeconomic variables that represent themacroeconomic features under consideration for the item of interest,determining the microeconomic factor by computing a linear combinationof observed or forecasted values of the microeconomic variables thatrepresent the microeconomic features specific to the industry; andcomputing the first computational component, the second computationalcomponent, and the third computational component of the residual valueforecasting model utilizing the baseline value for the item of interest,the locality-adjusted value of the item of interest having themodifications to the base configuration, the macroeconomic factor, andthe microeconomic factor thus determined.
 13. The system of claim 12,wherein the stored instructions are further translatable by theprocessor to perform: computing, utilizing a baseline value of a closestcompeting item in the competitive sets, an adjustment value; andgenerating a final forecasted residual value for the item of interest byadjusting the forecasted residual value for the item of interest withthe adjustment value.
 14. The system of claim 12, wherein themodifications to the base configuration of the item of interest comprisevalue-affecting changes to the item of interest at any time point. 15.The system of claim 12, wherein the stored instructions are furthertranslatable by the processor to perform: combining values received orobtained from the multiple data sources into a single value which givesmore weight to a data source or data type in which a higher confidenceis held, the combining including computing a combining equation at atime point in the reference period for a similar or substitute item thatcompetes with the item of interest in the industry.
 16. The system ofclaim 12, wherein the reference period is 36 months and wherein theconstant width of time intervals is two months.
 17. The system of claim11, wherein the second computational component further includes acoefficient for the macroeconomic factor and wherein the thirdcomputational component further includes a coefficient for themicroeconomic factor.
 18. The system of claim 11, wherein the usedmarket data comprises open auction data, closed auction data, andcertified pre-owned data.
 19. The system of claim 11, wherein thenon-industry-specific data comprises at least one of inflation,unemployment rate, gas prices, an economic index, interest rates, orindustry-wide used market supply.
 20. The system of claim 11, whereinthe industry-specific data comprises vehicle-specific data and whereinthe vehicle-specific data comprises modifications to the baseconfiguration of the item of interest.